To simulate Distributed Routing using MATLAB, we can design a network in which each node (router) independently computes their routing table according to the data shared with their adjacent nodes. In a distributed routing system, every single node iteratively exchanges routing data with its neighbors that updating their routing table rely on the received information, until every node join to optimal routes.
A generally utilized the algorithm for distributed routing that is a Distance Vector Routing such as the Bellman-Ford algorithm, in which each node sustains a distance vector to every other node and updates it depends on neighbours’ distance vectors. Stay in touch with us to get customized support.
Below is a step-by-step guide to configuring a distributed routing simulation using MATLAB.
Steps to Simulate Distributed Routing in MATLAB
- Define the Network Topology and Parameters:
- Configure an adjacency matrix to denote the network in which each entry signifies the cost of traveling amongst nodes. An inf entry indicates that there is no direct link among two nodes.
% Number of nodes in the network
numNodes = 5;
infCost = inf; % Use infinity to represent unreachable paths
% Define the adjacency matrix for link costs (example topology)
linkCosts = [0 4 infCost 2 infCost;
4 0 3 1 infCost;
infCost 3 0 5 2;
2 1 5 0 7;
infCost infCost 2 7 0];
% Display the network link costs for verification
disp(‘Initial Network Link Costs:’);
disp(linkCosts);
- Initialize Distance Vectors and Routing Tables:
- Every single node sustains a distance vector signifying the minimum cost to attain each other node. Introduce each node’s distance vector with infinity for unknown paths that setting their own distance to zero.
% Initialize distance vectors for each node
distanceVectors = inf(numNodes, numNodes); % Each row represents a node’s distance vector
for i = 1:numNodes
distanceVectors(i, i) = 0; % Distance to itself is zero
end
- Implement the Distributed Distance Vector Update Rule:
- Each node modernizes their distance vector by verifying the distance vectors of its neighbors, applying the Bellman Ford equation: distance(u,v)=min(distance(u,v),distance(u,w)+linkCost(w,v))\text{distance}(u, v) = \min(\text{distance}(u, v), \text{distance}(u, w) + \text{linkCost}(w, v))distance(u,v)=min(distance(u,v),distance(u,w)+linkCost(w,v))
- This rule is implemented iteratively until no more updates happen.
function [updatedDistanceVectors, changed] = updateDistanceVectors(distanceVectors, linkCosts, numNodes)
changed = false;
updatedDistanceVectors = distanceVectors;
% Loop over each node and update distances based on neighbors’ distance vectors
for u = 1:numNodes
for v = 1:numNodes
if linkCosts(u, v) < inf % Check if there’s a direct link
for w = 1:numNodes
% Bellman-Ford update rule
if updatedDistanceVectors(u, w) > distanceVectors(v, w) + linkCosts(u, v)
updatedDistanceVectors(u, w) = distanceVectors(v, w) + linkCosts(u, v);
changed = true;
end
end
end
end
end
end
- Run the Distributed Routing Simulation Until Convergence:
- In iteration, implement the distance vector update rule replicating the exchange of distance vectors amongst nodes. The simulation ends when no updates are created over every node that signifying convergence.
maxIterations = numNodes – 1; % Maximum number of iterations for convergence
for iter = 1:maxIterations
disp([‘Iteration ‘, num2str(iter)]);
[newDistanceVectors, changed] = updateDistanceVectors(distanceVectors, linkCosts, numNodes);
disp(‘Distance Vectors after this iteration:’);
disp(newDistanceVectors);
% If no changes occurred, the algorithm has converged
if ~changed
disp(‘Routing has converged’);
break;
end
% Update distance vectors for the next iteration
distanceVectors = newDistanceVectors;
end
- Display Final Routing Tables for Each Node:
- When the algorithm converges they show the final distance vectors for each node that denoting the minimum cost paths to every other node.
disp(‘Final Distance Vectors after Convergence:’);
for i = 1:numNodes
disp([‘Node ‘, num2str(i), ‘ Distance Vector: ‘, num2str(distanceVectors(i, :))]);
end
- Visualize the Network Topology and Routing Paths (Optional):
- Plot the network topology to envision the nodes, links, and the computed minimum-cost paths amongst nodes.
% Define node positions for visualization
nodePositions = [10 10; 20 10; 20 20; 10 20; 15 25];
figure;
hold on;
% Plot the network links and costs
for i = 1:numNodes
for j = i+1:numNodes
if linkCosts(i, j) < infCost
plot([nodePositions(i, 1), nodePositions(j, 1)], …
[nodePositions(i, 2), nodePositions(j, 2)], ‘k–‘);
midPoint = (nodePositions(i, 🙂 + nodePositions(j, :)) / 2;
text(midPoint(1), midPoint(2), num2str(linkCosts(i, j)), …
‘HorizontalAlignment’, ‘center’, ‘BackgroundColor’, ‘white’);
end
end
plot(nodePositions(i, 1), nodePositions(i, 2), ‘bo’); % Plot nodes
text(nodePositions(i, 1), nodePositions(i, 2), num2str(i), ‘VerticalAlignment’, ‘bottom’);
end
title(‘Network Topology with Link Costs’);
hold off;
Explanation of Key Components
- Distance Vector Initialization: Every single node begins with an initial distance vector, along with the cost to itself set to zero and every other path introduced to infinity.
- Distributed Distance Vector Update: Each node iteratively updates their distance vector by likening their own distances with those of its neighbors, which following the Bellman-Ford update rule.
- Convergence Check: The algorithm ends when no updates are created within iteration then showing that every nodes needs to converged to the shortest paths.
- Visualization: Envisioning the network topology supports elucidate the structure of the network and how routing data broadcasts to discover the optimal paths.
Possible Extensions
- Dynamic Network Conditions: Launch modifications to link costs or replicate the node failures to monitor how nodes adjust its distance vectors.
- Multi-hop Routing: Enable nodes to forward packets according to the computed paths that replicating real packet transfers over the network.
- Convergence Speed: Monitor the total of iterations is needed to converge under diverse network topologies to examine the scalability.
By using the above procedures, we had successfully completed the simulation of Distributed Routing Projects with the help of MATLAB environment. We provided basic approach with sample coding and extensions for this project. If you want extra details regarding this process we will offered it.
